1. Field of the Invention
The present invention relates to the automatic measurement of visually perceptible attributes of the person or people within the range of visual sensor coverage.
2. Background of the Invention
Companies are constantly looking for new ways to improve their product or service. Companies spend millions of dollars each year in an effort to understand their customers and the effect of in store advertisements and product positioning on the buying habits of their customers. Until now, businesses have had to rely on spot surveys and ineffective frequent buyer programs to try to understand who their customers are and what their customers' behaviors are in their establishment.
The conventional approaches for gathering the demographic information, which require the customers' feedback and sales people's input, such as using questionnaires, registration forms or electronic devices, are often found to be cumbersome to both customers and sales people.
U.S. Pat. No. 5,369,571 of Metts disclosed a method and apparatus for obtaining demographic information at the point of sale. In the disclosed prior art, the sales clerk had to input an assessment concerning certain demographic information about the customer to generate the demographic data, using a bar code scanner, a keypad with keys, or buttons. However, this increases the sales clerk's labor load, and the extra activities delay overall processes at the point of sale, which could be costly from the business point of view of the particular businesses facility. Furthermore, the assessment could vary depending on the personal viewpoints of the sales clerks, thus making the accumulated demographic data from different sales clerks over a period of time unreliable.
Thus, these problems in the prior art require an automatic and efficient approach for gathering the demographic information from the customers.
Computer vision algorithms have been shown to be an effective means for detecting people. For example, in Haritaoglu, Ismail and Flickner, Myron, “Attentive Billboards”, 2001 IEEE, pp. 162-167, the authors describe a method for detecting people and determining how long those people looked at a billboard. Also, in U.S. Pat. Application. No. 20020076100, the authors describe a method for detecting human figures in a digital image.
Other computer vision techniques have been shown to be able to extract relevant demographic features of people in an image. For example, in Moghaddam, Baback and Yang, Ming-Hsuan, “Gender Classification with Support Vector Machines”, 2000 Proc. of Int'l Conf. on Automatic Face and Gesture Recognition, the authors describe a technique using Support Vector Machines (SVM) for classifying a face image as a male or female person. In the U.S. Pat. No. 5,781,650, the authors describe a method for discerning the age of a person in an image. In Lyons, Michael J. et al, “Automatic Classification of Single Facial Images”, 1999 IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 12, pp. 1357-1362, the authors describe a method for discerning the ethnicity (or race) of a person in an image. Finally, in U.S. Pat. No. 6,188,777, the authors describe a means to extract a person's height, skin color, and clothing color from an image of a person. The combination of computer vision techniques such as the ones mentioned above allows for the possibility of connecting the visual information from a scene with a timestamp and a location marker to derive rich behavioral characteristics of the people in the scene and thus useful information.